By Swagata Roychowdhury, PhD

Editor’s Note: The tool discussed in this article does not give you all the answers or make choices for you. It is one way of helping you reflect on the career options open to you.

Many postdoctoral fellows and graduate students aspire to become a principal investigator (PI). But given the current statistics, less than 15 percent secure a tenured position within six years of obtaining a PhD, while about 18 percent find untenured employment.1 The situation is grim and unpredictable. So how can you estimate your likelihood of becoming a PI?

Enter the PI predictor. In a matter of seconds, the calculated probability of your future tenure is a couple of clicks away. The PI predictor is a machine-learning approach formulated by three early-career scientists that claims to predict your likelihood of becoming a PI.2 The authors collected data from over 25,000 scientists on PubMed and showed that fate is somewhat predictable when it comes to academia. The PI predictor takes into account several factors, all of which the authors found play a significant role in the academic hiring and tenure process. These factors include:

Your number of publications

Impact factor (IF) of the journals where the scientist has published (this factor carries more weight than the number of times a publication is cited)

Citations/IF, which is the number of publications that receive above average citations for the journal in which they have been published

h-index, which is a quantification of research output of a scientist

Apart from these, some non-publication factors are also added to the equation

Scientist’s gender (according to the author’s research, being a woman puts a scientist at a disadvantage)

Ranking of the university where you work

While these factors may seem intimidating, don’t let them prevent you from exploring your potential. For example, graduate student Katrina Furth wasn’t expecting to encounter a program that could determine her chances of becoming a PI—she was skeptical about the function of the model (especially it being so early in her research career). But she received a nice ego boost when program indicated her “PI probability” was quite high.

There are a few exceptions to the above rules. A larger number of co-authors can have a negative effect on the predictability measure. Even if scientists do not have any high impact factor publications, they still have a fair chance of becoming a PI—but only if they have twice the number of first-author publications compared to non-PI scientists. Not surprisingly, the model predicts that scientists from higher ranked institutions will see the fruits of their hard work earlier than scientists from lower ranked institutions.

Is it good to know your predicted probability as you job hunt? That is uncertain. But as postdoctoral fellow Robert Mitchell says, at least it does not give people false hopes. As you might expect, publication record is still the most important determining factor in academic success. But, at the end of the day, the authors suggest that if the work is exceptional, it will not go unnoticed, regardless of where it has been published.